1. Shortest Paths C B A E D F 3 2 8 5 4 7 1 9 Shortest Paths
5/19/2018 4:53 PM
Shortest Paths C B A E D F 3 2 8 5 4 7 1 9
Shortest Paths

2. Outline and Reading Weighted graphs (§7.1)
Shortest path problem
Shortest path properties
Dijkstra’s algorithm (§7.1.1)
Algorithm
Edge relaxation
The Bellman-Ford algorithm (§7.1.2)
Shortest paths in dags (§7.1.3)
All-pairs shortest paths (§7.2.1)
Shortest Paths

3. Weighted Graphs PVD ORD SFO LGA HNL LAX DFW MIA
In a weighted graph, each edge has an associated numerical value, called the weight of the edge
Edge weights may represent, distances, costs, etc.
Example:
In a flight route graph, the weight of an edge represents the distance in miles between the endpoint airports
849
PVD
1843
ORD
142
SFO
802
LGA
1743
1205
337
1387
HNL
2555
1099
LAX
1233
DFW
1120
MIA
Shortest Paths

4. Shortest Path Problem PVD ORD SFO LGA HNL LAX DFW MIA
Given a weighted graph and two vertices u and v, we want to find a path of minimum total weight between u and v.
Length of a path is the sum of the weights of its edges.
Example:
Shortest path between Providence and Honolulu
Applications
Internet packet routing
Flight reservations
Driving directions
849
PVD
1843
ORD
142
SFO
802
LGA
1743
1205
337
1387
HNL
2555
1099
LAX
1233
DFW
1120
MIA
Shortest Paths

5. Shortest Path Properties
Property 1:
A subpath of a shortest path is itself a shortest path
Property 2:
There is a tree of shortest paths from a start vertex to all the other vertices
Example:
Tree of shortest paths from Providence
849
PVD
1843
ORD
142
SFO
802
LGA
1743
1205
337
1387
HNL
2555
1099
LAX
1233
DFW
1120
MIA
Shortest Paths

6. Dijkstra’s Algorithm
The distance of a vertex v from a vertex s is the length of a shortest path between s and v
Dijkstra’s algorithm computes the distances of all the vertices from a given start vertex s
Assumptions:
the graph is connected
the edges are undirected
the edge weights are nonnegative
We grow a “cloud” of vertices, beginning with s and eventually covering all the vertices
We store with each vertex v a label d(v) representing the distance of v from s in the subgraph consisting of the cloud and its adjacent vertices
At each step
We add to the cloud the vertex u outside the cloud with the smallest distance label, d(u)
We update the labels of the vertices adjacent to u
Shortest Paths

7. Edge Relaxation Consider an edge e = (u,z) such that d(z) = 75
u is the vertex most recently added to the cloud
z is not in the cloud
The relaxation of edge e updates distance d(z) as follows:
d(z)  min{d(z),d(u) + weight(e)}
d(u) = 50 10
d(z) = 75 e u s z
d(u) = 50 10
d(z) = 60 u e s z
Shortest Paths

8. Example C B A E D F 3 2 8 5 4 7 1 9 A 4 8 2 8 2 4 7 1 B C D 3 9   2 5 E F A 4 A 4 8 8 2 2 8 2 3 7 2 3 7 1 7 1 B C D B C D 3 9 3 9 5 11 5 8 2 5 2 5 E F E F
Shortest Paths

9. Example (cont.) A 4 8 2 7 2 3 7 1 B C D 3 9 5 8 2 5 E F A 4 8 2 7 2 3A 4 8 2 7 2 3 7 1 B C D 3 9 5 8 2 5 E F A 4 8 2 7 2 3 7 1 B C D 3 9 5 8 2 5 E F
Shortest Paths

10. Dijkstra’s Algorithm
Algorithm DijkstraDistances(G, s)
Q  new heap-based priority queue
for all v  G.vertices()
if v = s
setDistance(v, 0)
else
setDistance(v, )
l  Q.insert(getDistance(v), v)
setLocator(v,l)
while Q.isEmpty()
u  Q.removeMin()
for all e  G.incidentEdges(u)
{ relax edge e }
z  G.opposite(u,e)
r  getDistance(u) + weight(e)
if r < getDistance(z)
setDistance(z,r) Q.replaceKey(getLocator(z),r)
A priority queue stores the vertices outside the cloud
Key: distance
Element: vertex
Locator-based methods
insert(k,e) returns a locator
replaceKey(l,k) changes the key of an item
We store two labels with each vertex:
Distance (d(v) label)
locator in priority queue
Shortest Paths

11. Analysis Graph operations Label operations Priority queue operations
Method incidentEdges is called once for each vertex
Label operations
We set/get the distance and locator labels of vertex z O(deg(z)) times
Setting/getting a label takes O(1) time
Priority queue operations
Each vertex is inserted once into and removed once from the priority queue, where each insertion or removal takes O(log n) time
The key of a vertex in the priority queue is modified at most deg(w) times, where each key change takes O(log n) time
Dijkstra’s algorithm runs in O((n + m) log n) time provided the graph is represented by the adjacency list structure
Recall that Sv deg(v) = 2m
The running time can also be expressed as O(m log n) since the graph is connected
Shortest Paths

12. Extension
Using the template method pattern, we can extend Dijkstra’s algorithm to return a tree of shortest paths from the start vertex to all other vertices
We store with each vertex a third label:
parent edge in the shortest path tree
In the edge relaxation step, we update the parent label
Algorithm DijkstraShortestPathsTree(G, s) …
for all v  G.vertices()
setParent(v, )
for all e  G.incidentEdges(u)
{ relax edge e }
z  G.opposite(u,e)
r  getDistance(u) + weight(e)
if r < getDistance(z)
setDistance(z,r)
setParent(z,e)
Q.replaceKey(getLocator(z),r)
Shortest Paths

13. Why Dijkstra’s Algorithm Works
Dijkstra’s algorithm is based on the greedy method. It adds vertices by increasing distance.
Suppose it didn’t find all shortest distances. Let F be the first wrong vertex the algorithm processed.
When the previous node, D, on the true shortest path was considered, its distance was correct.
But the edge (D,F) was relaxed at that time!
Thus, so long as d(F)>d(D), F’s distance cannot be wrong. That is, there is no wrong vertex. A 8 4 2 7 2 3 7 1 B C D 3 9 5 8 2 5 E F
Shortest Paths

14. Why It Doesn’t Work for Negative-Weight Edges
Dijkstra’s algorithm is based on the greedy method. It adds vertices by increasing distance. A 8 4
If a node with a negative incident edge were to be added late to the cloud, it could mess up distances for vertices already in the cloud. 6 7 5 4 7 1 B C D -8 5 9 2 5 E F
C’s true distance is 1, but it is already in the cloud with d(C)=5!
Shortest Paths

15. Bellman-Ford Algorithm
Works even with negative-weight edges
Must assume directed edges (for otherwise we would have negative-weight cycles)
Iteration i finds all shortest paths that use i edges.
Running time: O(nm).
Can be extended to detect a negative-weight cycle if it exists
How?
Algorithm BellmanFord(G, s)
for all v  G.vertices()
if v = s
setDistance(v, 0)
else
setDistance(v, )
for i  1 to n-1 do
for each e  G.edges()
{ relax edge e }
u  G.origin(e)
z  G.opposite(u,e)
r  getDistance(u) + weight(e)
if r < getDistance(z)
setDistance(z,r)
Shortest Paths

16. Nodes are labeled with their d(v) values
Bellman-Ford Example
Nodes are labeled with their d(v) values 4 4 8 8 -2 -2 8 -2 4 7 1 7 1       3 9 3 9 -2 5 -2 5     4 4 8 8 -2 -2 5 7 1 -1 7 1 8 -2 4 5 -2 -1 1 3 9 3 9 6 9 4 -2 5 -2 5   1 9
Shortest Paths

17. DAG-based Algorithm Works even with negative-weight edges
Algorithm DagDistances(G, s)
for all v  G.vertices()
if v = s
setDistance(v, 0)
else
setDistance(v, )
Perform a topological sort of the vertices
for u  1 to n do {in topological order}
for each e  G.outEdges(u)
{ relax edge e }
z  G.opposite(u,e)
r  getDistance(u) + weight(e)
if r < getDistance(z)
setDistance(z,r)
Works even with negative-weight edges
Uses topological order
Doesn’t use any fancy data structures
Is much faster than Dijkstra’s algorithm
Running time: O(n+m).
Shortest Paths

18. Nodes are labeled with their d(v) values1
DAG Example
Nodes are labeled with their d(v) values 1 1 4 4 8 8 -2 -2 4 8 -2 4 3 7 2 1 3 7 2 1 4       3 9 3 9 -5 5 -5 5     6 5 6 5 1 1 4 4 8 8 -2 -2 5 3 7 2 1 4 -1 3 7 2 1 4 8 -2 4 5 -2 -1 9 3 3 9 1 7 4 -5 5 -5 5   1 7 6 5 6 5
Shortest Paths
(two steps)

19. All-Pairs Shortest Paths
Find the distance between every pair of vertices in a weighted directed graph G.
We can make n calls to Dijkstra’s algorithm (if no negative edges), which takes O(nmlog n) time.
Likewise, n calls to Bellman-Ford would take O(n2m) time.
We can achieve O(n3) time using dynamic programming (similar to the Floyd-Warshall algorithm).
Algorithm AllPair(G) {assumes vertices 1,…,n}
for all vertex pairs (i,j)
if i = j
D0[i,i]  0
else if (i,j) is an edge in G
D0[i,j]  weight of edge (i,j)
else
D0[i,j]  + 
for k  1 to n do
for i  1 to n do
for j  1 to n do
Dk[i,j]  min{Dk-1[i,j], Dk-1[i,k]+Dk-1[k,j]}
return Dn
Uses only vertices numbered 1,…,k
(compute weight of this edge) i
Uses only vertices
numbered 1,…,k-1 j
Uses only vertices
numbered 1,…,k-1 k
Shortest Paths